4,980 research outputs found
A New Algorithm For Difference Image Analysis
In the context of difference image analysis (DIA), we present a new method
for determining the convolution kernel matching a pair of images of the same
field. Unlike the standard DIA technique which involves modelling the kernel as
a linear combination of basis functions, we consider the kernel as a discrete
pixel array and solve for the kernel pixel values directly using linear
least-squares. The removal of basis functions from the kernel model is
advantageous for a number of compelling reasons. Firstly, it removes the need
for the user to specify such functions, which makes for a much simpler user
application and avoids the risk of an inappropriate choice. Secondly, basis
functions are constructed around the origin of the kernel coordinate system,
which requires that the two images are perfectly aligned for an optimal result.
The pixel kernel model is sufficiently flexible to correct for image
misalignments, and in the case of a simple translation between images, image
resampling becomes unnecessary. Our new algorithm can be extended to spatially
varying kernels by solving for individual pixel kernels in a grid of image
sub-regions and interpolating the solutions to obtain the kernel at any one
pixel.Comment: MNRAS Letters Accepte
Disc galaxies with multiple triaxial structures. II. JHK surface photometry and numerical simulations
We present detailed JHK surface photometry with ellipse fits of 13 galaxies
selected from previous optical observations as likely candidates for having a
secondary bar or a triaxial bulge within the primary bar. We have found 7
double-barred galaxies, 3 double-barred galaxies with an additional
intermediate structure with twisted isophotes, and 3 galaxies with a bar and
central twisted isophotes. A global analysis of the structural parameter
characteristics in the I- and K-bands is presented. Various numerical models of
galaxies with bars within bars are also analysed using the ellipse fitting
technique and compared to the observations. A thorough review of the possible
hypotheses able to explain this phenomenon is given with emphasis on the most
likely ones.Comment: 12 pages, AATEX. Accepted for publication in A&A. Large color
postscript figures omitted (Figs. 1), figures 2-9 included; gzip'ed
postscript files of the paper and Figs. 1 available via anonymous ftp at
ftp://obsftp.unige.ch/pub/fri/aasjhk/ , files fri_aasjhk.ps.gz and ngc*.ps.g
Estimating Photometric Redshifts Using Support Vector Machines
We present a new approach to obtaining photometric redshifts using a kernel
learning technique called Support Vector Machines (SVMs). Unlike traditional
spectral energy distribution fitting, this technique requires a large and
representative training set. When one is available, however, it is likely to
produce results that are comparable to the best obtained using template fitting
and artificial neural networks. Additional photometric parameters such as
morphology, size and surface brightness can be easily incorporated. The
technique is demonstrated using samples of galaxies from the Sloan Digital Sky
Survey Data Release 2 and the hybrid galaxy formation code GalICS. The RMS
error in redshift estimation is for both samples. The strengths and
limitations of the technique are assessed.Comment: 10 pages, 3 figures, to appear in the PASP, minor typos fixed to make
consistent with published versio
- …